Numerical dark-field imaging using deep-learning.
Numerical dark-field imaging using deep-learning.
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DOI：
10.1364/oe.401786
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发表时间：
2020-11
期刊：
影响因子：
3.8
通讯作者：
Zhang Meng;Liqi Ding;Shaotong Feng;Fangjian Xing;Shou-Ping Nie;Jun Ma;G. Pedrini;Caojin Yuan
中科院分区：
文献类型：
--
作者：
Zhang Meng;Liqi Ding;Shaotong Feng;Fangjian Xing;Shou-Ping Nie;Jun Ma;G. Pedrini;Caojin Yuan
暗场显微镜是一种强大的技术，提高成像分辨率和对比度的小未染色样品。在这项研究中，我们报告了一种基于端到端卷积神经网络的方法，从低分辨率的亮场图像中重建高分辨率的暗场图像。通过训练相应的网络，可以得到理论上难以推导的明暗场关系。通过专门设计的多路复用图像系统同时获得训练数据，即同一目标视图的匹配明暗场图像。由于不需要图像配准这一数据准备的关键步骤，可以在很大程度上避免人工误差。训练后，将传统的亮场图像作为该网络的输入，生成高分辨率的数值暗场图像。通过分辨率测试靶和重建的生物组织数值暗场图像的定量分析，验证了该方法的有效性。实验结果表明，所提出的基于学习的方法可以实现从明场图像到暗场图像的转换，从而有效地实现高分辨率的数值暗场成像。该网络对不同类型的样本具有普适性。此外，我们还验证了该方法具有良好的抗噪声性能，并且不受实验设置引起的不稳定因素的影响。
Dark-field microscopy is a powerful technique for enhancing the imaging resolution and contrast of small unstained samples. In this study, we report a method based on end-to-end convolutional neural network to reconstruct high-resolution dark-field images from low-resolution bright-field images. The relation between bright- and dark-field which was difficult to deduce theoretically can be obtained by training the corresponding network. The training data, namely the matched bright- and dark-field images of the same object view, are simultaneously obtained by a special designed multiplexed image system. Since the image registration work which is the key step in data preparation is not needed, the manual error can be largely avoided. After training, a high-resolution numerical dark-field image is generated from a conventional bright-field image as the input of this network. We validated the method by the resolution test target and quantitative analysis of the reconstructed numerical dark-field images of biological tissues. The experimental results show that the proposed learning-based method can realize the conversion from bright-field image to dark-field image, so that can efficiently achieve high-resolution numerical dark-field imaging. The proposed network is universal for different kinds of samples. In addition, we also verify that the proposed method has good anti-noise performance and is not affected by the unstable factors caused by experiment setup.